Ye Jun Lee

Korea Institute of Robot and Convergence

Papers

Total Citations

H-Index

About

Ye Jun Lee is a researcher in autonomous robotics and computer vision, with a focus on real-time object recognition and tracking for mobile robotic systems. His most cited work, "Variance Optimization Based on Guided Anchor Siamese Network for Target-of-interest Object Recognition in Autonomous Mobile Robots" (2023), introduces a novel approach that combines variance optimization with a guided anchor Siamese network to enhance the accuracy and efficiency of target recognition in dynamic environments. This contribution addresses critical challenges in robotic perception, enabling more reliable autonomous navigation and interaction. While his citation count is currently modest, with 4 citations for this key paper, his work represents a meaningful step toward robust, real-time object detection in resource-constrained mobile platforms. Lee’s research sits at the intersection of deep learning, optimization, and robotics, and his methodology offers practical insights for developing intelligent systems capable of operating in unstructured settings. As the field of autonomous robotics continues to expand, his contributions are poised to gain further recognition, particularly among engineers and researchers working on embedded vision systems and adaptive robotic control.